How to convert data into timeseries for column groups

Question

I have data with timestamps. Users do tasks, and the timestamp is recorded. Each user is identified by a ‘uid’. I want to convert this data into 10-minute granular time series, but for each user separately. So, timestamp goes in chronological order for uid=1 separately, then for uid=2 and so on.

From:

timestamp          uid    var
2020-01-01 10:00    1     10
2020-01-01 10:04    2     20 
2020-01-01 20:02    2     15
2020-01-01 21:20    1     10
.. 
2020-01-15 23:12    1     5

To:

timestamp           uid   var
2020-01-01 10:00     1    10
2020-01-01 10:10     1    NaN
2020-01-01 10:20     1    NaN
...
2020-01-15 23:10     1    5
2020-01-01 10:00     2    20
2020-01-01 10:10     2    NaN
2020-01-01 10:20     2    NaN
...

Asked By: prof31

||

Source

Answer 1

grouped by uid column and resample 10T

import numpy as np

(df.groupby('uid')
 .resample(rule='10T')['var'].sum()
 .reset_index(level=0)
 .replace({0: np.NaN}))

Answered By: Panda Kim

How to convert data into timeseries for column groups

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